code: def run_model(
      self,
      model,
      inputs_list,
      tolerate_small_mismatch=False,
      do_constant_folding=True,
      dynamic_axes=None,
      output_names=None,
      input_names=None,
  ):
      model.eval()
  
      onnx_io = io.BytesIO()
      # export to onnx with the first input
      torch.onnx.export(
          model,
          inputs_list[0],
          onnx_io,
          do_constant_folding=do_constant_folding,
          opset_version=12,
          dynamic_axes=dynamic_axes,
          input_names=input_names,
          output_names=output_names,
      )
      # validate the exported model with onnx runtime
      for test_inputs in inputs_list:
          with torch.no_grad():
              if isinstance(test_inputs, torch.Tensor) or isinstance(
                  test_inputs, list
              ):
                  test_inputs = (
                      nested_tensor_from_tensor_list(test_inputs),
                  )
              test_ouputs = model(*test_inputs)
              if isinstance(test_ouputs, torch.Tensor):
                  test_ouputs = (test_ouputs,)
          self.ort_validate(
              onnx_io, test_inputs, test_ouputs, tolerate_small_mismatch
          )
  
imports: import io
  import unittest
  import torch
  from torch import nn, Tensor
  from typing import List
  from models.matcher import HungarianMatcher
  from models.position_encoding import PositionEmbeddingSine, PositionEmbeddingLearned
  from models.backbone import Backbone, Joiner, BackboneBase
  from util import box_ops
  from util.misc import nested_tensor_from_tensor_list
  from hubconf import detr_resnet50, detr_resnet50_panoptic
label_desc: Use image node_id 2 for calling the ONNXExporterTester obj's underlying member method code with example usage: obj.run_model(model, inputs_list, tolerate_small_mismatch, do_constant_folding, dynamic_axes, output_names, input_names) without return types